Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

Published Date: Jan 11, 2021

Abstract

PURPOSE: To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserve the image texture of conventional filtered back projection (FBP) at reduced dose levels attained by ASIR-V in chest CT.

Authors

Caro Franck

Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium; mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium. Electronic address: caro.franck@uza.be.
Guozhi Zhang

Department of Radiology, KU Leuven University Hospitals Leuven, Leuven, Belgium.
Paul Deak

GE Healthcare, Glattbrugg, Switzerland.
Federica Zanca

Palindromo Consulting, Leuven, Belgium.

Keywords

Algorithms Deep Learning Image Processing, Computer-Assisted Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (33445125)

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.

Abstract

Authors

Keywords

External Resources

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